System for monitoring individuals as they age in place

ABSTRACT

A computer-implemented method, and related system, for monitoring the wellbeing of an individual by providing eyewear that includes at least one sensor for monitoring the motion of the user. In various embodiments, the system receives data generated by the at least one sensor, uses the data to determine the user&#39;s movements using the received data, and compares the user&#39;s movements to previously established movement patterns of the user. If the system detects one or more inconsistencies between the user&#39;s current movements as compared to the previously established movement patterns of the user, the system may notify the user or a third party of the detected one or more inconsistencies. The system may similarly monitor a user&#39;s compliance with a medical regime and notify the user or a third party of the user&#39;s compliance with the regime.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/562,454, filed Dec. 5, 2014, entitled “System for MonitoringIndividuals as They Age in Place,” which claims the benefit of U.S.Provisional Patent Application No. 62/046,406, filed Sep. 5, 2014,entitled, “Wearable Health Computer Apparatus, Systems, and RelatedMethods,” the disclosures of which are hereby incorporated herein byreference in their entirety.

BACKGROUND

Being able to monitor elderly individuals who live independently at homehas become increasingly important due, in part, to the high cost ofelder care facilities. Accordingly, there is a need for improved systemsand methods for monitoring the activities and wellbeing of elderlyindividuals living at home. There is a similar need for monitoring theactivities and wellbeing of individuals with special needs livingoutside of an institutional setting. Various embodiments of the presentsystems and methods recognize and address the foregoing considerations,and others, of prior art systems and methods.

SUMMARY OF THE VARIOUS EMBODIMENTS

A computer-implemented method of monitoring the wellbeing of anindividual according to various embodiments comprises the steps of: (1)providing a user with computerized eyewear comprising at least onesensor for monitoring the motion of the user; (2) receiving datagenerated by the at least one sensor; (3) determining the user'smovements using the received data; (4) comparing the user's movements topreviously established one or more movement patterns for the user; (5)determining whether one or more inconsistencies exist between thecurrent user's movements and the previously-established one or moremovement patterns; and (6) at least partially in response to determiningthat such one or more inconsistencies exist, notifying a recipientselected from a group consisting of: the user and/or a third party ofthe detected one or more inconsistencies.

A computer-implemented method of monitoring the wellbeing of anindividual according to further embodiments comprises the steps of: (1)providing a user with a computerized wearable device comprising at leastone sensor for monitoring actions taken by a user; (2) receiving amedicine regime associated with the user; (3) receiving data generatedby the at least of the wearable device's sensors; (4) analyzing thereceived data generated by the at least one sensor to determine: (a) thetype of medicine taken by the wearer; (b) the time the medicine is takenby the wearer; and/or (c) the dose of medicine taken by the wearer; (5)comparing the medicine regime for the user to the determined one or moreof the type of medicine taken by the wearer, the time the medicine istaken by the wearer, and/or dose of medicine taken by the wearer; (6)detecting one or more inconsistencies between the medicine regimeassociated with the user and the determined one or more of the type ofmedicine taken by the user, the time the medicine is taken by the user,and/or the dose of medicine taken by the user; (7) notifying the userand/or third party of the detected inconsistencies.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of systems and methods for assessing a user'sactivities and movements are described below. In the course of thisdescription, reference will be made to the accompanying drawings, whichare not necessarily drawn to scale and wherein:

FIG. 1 is a block diagram of a Behavior Pattern Analysis System inaccordance with an embodiment of the present system;

FIG. 2 is a block diagram of the Pattern Analysis Server of FIG. 1;

FIG. 3 is a flowchart that generally illustrates various steps executedby a Behavior Pattern Analysis Module according to a particularembodiment; and

FIG. 4 is a perspective view of computerized eyewear according to aparticular embodiment.

DETAILED DESCRIPTION OF SOME EMBODIMENTS

Various embodiments will now be described more fully hereinafter withreference to the accompanying drawings. It should be understood that theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the invention to thoseskilled in the art. Like numbers refer to like elements throughout.

Overview

A wearable health monitoring system, according to various embodiments,may include a suitable wearable device that is configured to monitor oneor more movements, activities, and/or health attributes of a wearer(e.g., user). Suitable wearable devices may include, for example: (1)pair of eyewear (e.g., goggles or eyeglasses); (2) one or more contactlenses; (3) a wristwatch; (4) an article of clothing (e.g., such as asuitable shirt, pair of pants, undergarment, compression sleeve, etc.);(5) footwear; (6) a hat; (7) a helmet; (8) an adhesive strip or othertag that may be selectively attached to an individual or theindividual's clothing; (9) a computing device that is embedded into aportion of an individual's body (e.g., under the individual's skin, orwithin a medical device, such as a pacemaker); (10) an orthopedic cast,or (11) any other suitable wearable item. In a particular example, awearable health monitoring system embodied as a pair of eyewear mayenable the system to monitor what an individual is sensing (e.g.,touching, seeing, hearing, smelling, and/or tasting) based at least inpart on a proximity of the eyewear to the wearer's sensory systems(e.g., skin, eyes, mouth, ears, nose) when worn by the wearer.

In various embodiments, the system comprises one or more sensors thatare configured to determine one or more current physical attributes ofthe wearer (e.g., heart rate, brain wave activity, movement, bodytemperature, blood pressure, oxygen saturation level, etc. . . . ). Theone or more sensors may include, for example: (1) one or more heart ratemonitors; (2) one or more electrocardiograms (EKG); (3), one or moreelectroencephalograms (EEG); (4) one or more pedometers; (5) one or morethermometers; (6) one or more transdermal transmitter sensors; (7) oneor more front-facing cameras; (8) one or more eye-facing cameras; (9)one or more microphones; (10) one or more accelerometers; (11) one ormore blood pressure sensors; (12) one or more pulse oximeters; (13) oneor more respiratory rate sensors; (14) one or more blood alcoholconcentration (BAC) sensors; (15) one or more near-field communicationsensors; (16) one or more motion sensors; (17) one or more gyroscopes;(18) one or more geomagnetic sensors; (19) one or more globalpositioning system sensors; (20) one or more impact sensors; and/or (21)any other suitable one or more sensors.

In particular embodiments, the system is configured to gather data, forexample, using the one or more sensors, about the wearer (e.g., such asthe wearer's body temperature, balance, heart rate, level of physicalactivity, diet (e.g., food recently eaten), compliance with a prescribedmedical regimen (e.g., medications recently taken), position, movements(e.g., body movements, facial muscle movements), location, distancetraveled, etc.). In various embodiments, the system is configured to,for example: (1) store the gathered data associated with the user; (2)provide the data to one or more medical professionals, for example, toaid in the diagnosis and/or treatment of the user; (3) use the data topredict one or more medical issues with the user (e.g., the illness ordeath of the user); and/or (4) take any other suitable action based atleast in part on the gathered data.

In a particular implementation, the system's wearable device is a pairof computerized eyewear that comprises one or more sensors formonitoring one or more day-to-day activities of an elderly individual asthey “age in place” (e.g., they live in a non-institutional setting). Inparticular embodiments, the one or more sensors are coupled to (e.g.,connected to, embedded in, etc.) the pair of glasses, which may be, forexample, a pair of computerized or non-computerized eyeglasses. Inparticular embodiments, the individual is a senior citizen who lives atleast substantially independently.

In particular embodiments, the wearable computing device comprises oneor more location sensors (e.g., geomagnetic sensors, etc.), motionsensors (e.g., accelerometers, gyroscopes, magnetic sensors, pressuresensors, etc.), and/or impact sensors that are adapted to sense themovement and location of the individual. In various embodiments, thewearable device is adapted to facilitate the transmission of thismovement information to a remote computing device (e.g., a handheldcomputing device, an automated dialing device, a central server, or anyother suitable smart device that may, in various embodiments, contain awireless communications device that can connect to the wearablecomputing device) that analyzes the information to determine whether theindividual's movement patterns are consistent with the individual'stypical (e.g., past) movement patterns. If the movement patterns areinconsistent with the individual's typical movement patterns, the systemmay, for example, generate and transmit an alert to a third party (e.g.,a physician, relative of the individual, other caretaker, police, etc.)informing the third party of the irregularities in the individual'smovement. The third party may then, for example, check on the individualto make sure that the individual does not require assistance.

In further embodiments, the wearable device may be adapted, for example,to monitor: (1) an individual's compliance with a prescribed treatmentplan (e.g., compliance with a medication schedule); (2) an individual'scompliance with a diet; and/or (3) whether an individual leaves aprescribed area defined by a geo-fence (e.g., a virtual fence). Thesystem may do this, for example, by using any suitable sensors (e.g.,location sensors, cameras, etc. . . . ) associated with the wearabledevice.

Exemplary Technical Platforms

As will be appreciated by one skilled in the relevant field, the presentsystems and methods may be, for example, embodied as a computer system,a method, or a computer program product. Accordingly, variousembodiments may be entirely hardware or a combination of hardware andsoftware. Furthermore, particular embodiments may take the form of acomputer program product stored on a computer-readable storage mediumhaving computer-readable instructions (e.g., software) embodied in thestorage medium. Various embodiments may also take the form ofInternet-implemented computer software. Any suitable computer-readablestorage medium may be utilized including, for example, hard disks,compact disks, DVDs, optical storage devices, and/or magnetic storagedevices.

Various embodiments are described below with reference to block diagramand flowchart illustrations of methods, apparatuses, (e.g., systems),and computer program products. It should be understood that each blockof the block diagrams and flowchart illustrations, and combinations ofblocks in the block diagrams and flowchart illustrations, respectively,can be implemented by a computer executing computer programinstructions. These computer program instructions may be loaded onto ageneral purpose computer, a special purpose computer, or otherprogrammable data processing apparatus that can direct a computer orother programmable data processing apparatus to function in a particularmanner such that the instructions stored in the computer-readable memoryproduce an article of manufacture that is configured for implementingthe functions specified in the flowchart block or blocks.

The computer instructions may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon a user's computer and partly on a remote computer, or entirely on theremote computer or server. In the latter scenario, the remote computermay be connected to the user's computer through any type of network,including but not limited to: (1) a local area network (LAN); (2) a widearea network (WAN); (3) a cellular network; or (4) the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider).

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner such that the instructions stored in the computer-readable memoryproduce an article of manufacture that is configured for implementingthe function specified in the flowchart block or blocks. The computerprogram instructions may also be loaded onto a computer or otherprogrammable data processing apparatus to cause a series of operationalsteps to be performed on the computer or other programmable apparatus toproduce a computer-implemented process (e.g., method) such that theinstructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Example System Architecture

FIG. 1 is a block diagram of a Behavior Pattern Analysis System 100according to particular embodiments. As may be understood from thisfigure, the Behavior Pattern Analysis System 100 includes One or MoreNetworks 115, One or More Third Party Servers 50, a Pattern AnalysisServer 120 that includes a Behavior Pattern Analysis Module 300, aMovement Information Database 140, One or More Remote Computing Devices154 (e.g., such as a smart phone, a tablet computer, a wearablecomputing device, a laptop computer, a desktop computer, a Bluetoothdevice, an automated dialing apparatus, etc.), and One or More WearableHealth Monitoring Devices 156, which may, for example, be embodied asone or more of eyewear, headwear, clothing, a watch, a hat, a helmet, acast, an adhesive bandage, a piece of jewelry (e.g., a ring, earring,necklace, bracelet, etc.), or any other suitable wearable device. Inparticular embodiments, the one or more computer networks 115 facilitatecommunication between the One or More Third Party Servers 50, thePattern Analysis Server 120, the Movement Information Database 140, theOne or More Remote Computing Devices 154, and the one or more HealthMonitoring Devices 156.

The one or more networks 115 may include any of a variety of types ofwired or wireless computer networks such as the Internet, a privateintranet, a mesh network, a public switch telephone network (PSTN), orany other type of network (e.g., a network that uses Bluetooth or nearfield communications to facilitate communication between computingdevices). The communication link between the One or More RemoteComputing Devices 154 and the Pattern Analysis Server 120 may be, forexample, implemented via a Local Area Network (LAN) or via the Internet.

FIG. 2 illustrates a diagrammatic representation of the architecture forthe Pattern Analysis Server 120 that may be used within the BehaviorPattern Analysis System 100. It should be understood that the computerarchitecture shown in FIG. 2 may also represent the computerarchitecture for any one of the One or More Remote Computing Devices154, one or more Third Party Servers 50, and One or More HealthMonitoring Devices 156 shown in FIG. 1. In particular embodiments, thePattern Analysis Server 120 may be suitable for use as a computer withinthe context of the Behavior Pattern Analysis System 100 that isconfigured for monitoring the behavior (e.g., movements, location,eating and sleeping habits) of the wearer.

In particular embodiments, the Pattern Analysis Server 120 may beconnected (e.g., networked) to other computing devices in a LAN, anintranet, an extranet, and/or the Internet as shown in FIG. 1. As notedabove, the Pattern Analysis Server 120 may operate in the capacity of aserver or a client computing device in a client-server networkenvironment, or as a peer computing device in a peer-to-peer (ordistributed) network environment. The Pattern Analysis Server 120 may bea desktop personal computing device (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a network router, a switch or bridge, or any other computingdevice capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that computing device.Further, while only a single computing device is illustrated, the term“computing device” shall also be interpreted to include any collectionof computing devices that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

An exemplary Pattern Analysis Server 120 includes a processing device202, a main memory 204 (e.g., read-only memory (ROM), flash memory,dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) orRambus DRAM (RDRAM), etc.), a static memory 206 (e.g., flash memory,static random access memory (SRAM), etc.), and a data storage device218, which communicate with each other via a bus 232.

The processing device 202 represents one or more general-purpose orspecific processing devices such as a microprocessor, a centralprocessing unit (CPU), or the like. More particularly, the processingdevice 202 may be a complex instruction set computing (CISC)microprocessor, reduced instruction set computing (RISC) microprocessor,very long instruction word (VLIW) microprocessor, or processorimplementing other instruction sets, or processors implementing acombination of instruction sets. The processing device 202 may also beone or more special-purpose processing devices such as an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a digital signal processor (DSP), network processor, or thelike. The processing device 202 may be configured to execute processinglogic 226 for performing various operations and steps discussed herein.

The Pattern Analysis Server 120 may further include a network interfacedevice 208. The Pattern Analysis Server 120 may also include a videodisplay unit 210 (e.g., a liquid crystal display (LCD) or a cathode raytube (CRT)), an alpha-numeric input device 212 (e.g., a keyboard), acursor control device 214 (e.g., a mouse), and a signal generationdevice 216 (e.g., a speaker).

The data storage device 218 may include a non-transitory computingdevice-accessible storage medium 230 (also known as a non-transitorycomputing device-readable storage medium or a non-transitory computingdevice-readable medium) on which is stored one or more sets ofinstructions (e.g., the Behavior Pattern Analysis Module 300) embodyingany one or more of the methodologies or functions described herein. Theone or more sets of instructions may also reside, completely or at leastpartially, within the main memory 204 and/or within the processingdevice 202 during execution thereof by the Pattern Analysis Server120—the main memory 204 and the processing device 202 also constitutingcomputing device-accessible storage media. The one or more sets ofinstructions may further be transmitted or received over a network 115via a network interface device 208.

While the computing device-accessible storage medium 230 is shown in anexemplary embodiment to be a single medium, the term “computingdevice-accessible storage medium” should be understood to include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore sets of instructions. The term “computing device-accessible storagemedium” should also be understood to include any medium that is capableof storing, encoding, or carrying a set of instructions for execution bythe computing device and that causes the computing device to include anyone or more of the methodologies of the present invention. The term“computing device-accessible storage medium” should accordingly beunderstood to include, but not be limited to, solid-state memories,optical and magnetic media, etc.

Exemplary System Platform

As noted above, a system, according to various embodiments, is adaptedto monitor one or more patterns of behavior and/or one or more locationsof a user of a wearable device. Various aspects of the system'sfunctionality may be executed by certain system modules, including theBehavior Pattern Analysis Module 300. The Behavior Pattern AnalysisModule 300 is discussed in greater detail below.

Behavior Pattern Analysis Module

FIG. 3A is a flow chart of operations performed by an exemplary BehaviorPattern Analysis Module 300, which may, for example, run on the PatternAnalysis Server 120, or any suitable computing device (such as the Oneor More Health Monitoring Devices 156, a handheld computing devicecoupled to communicate with the One or More Health Monitoring Devices156 or a suitable mobile computing device). In particular embodiments,the Behavior Pattern Analysis Module 300 may assess a user's behaviorand determine the user's location to provide this information to theuser or to a third party.

The system begins, in various embodiments, at Step 305 by providing auser with computerized eyewear comprising at least one sensor formonitoring one or more behaviors of the user and/or any suitableattributes of the user. In various embodiments, the at least one sensormay include a location sensor (e.g., a GPS unit), an accelerometer, aheart rate monitor, one or more electrocardiogram (EKG), anelectroencephalogram (EEG), a pedometer, a thermometer, a front-facingcamera, an eye-facing camera, a microphone, an accelerometer, a bloodpressure sensor, a pulse oximeter, a near-field communication sensor, amotion sensor, a gyroscope, a geomagnetic sensor, an impact sensor,and/or any other suitable sensor. In particular embodiments, thecomputerized eyewear further comprises: a motion sensor, anaccelerometer, a GPS unit, a gyroscope, and/or a front-facing camera.

In particular embodiments, the sensors may be coupled to the eyewear inany suitable way. For example, in various embodiments, the sensors maybe physically embedded into, or otherwise coupled to the eyewear. Insome embodiments, the sensors may be positioned: (1) along the brow barof the eyewear; (2) along the temples of the eyewear; (3) adjacent thelenses of the eyewear; and/or (4) in any other suitable location.

In particular embodiments: (1) the sensors are coupled to a wireless(e.g., Bluetooth, near-field communications, Wi-Fi, etc.) device that isconfigured to transmit one or more signals obtained from the one or moresensors to a handheld wireless device (e.g., a smartphone, a tablet, anautomated dialing device, etc.); and (2) the step of receiving one ormore signals from the one or more sensors further comprises receivingthe one or more signals from the wireless handheld device via theInternet. In particular embodiments, one or more of the sensors may beselectively detachable from the eyewear, or other wearable device. Forexample, if a user does not need the temperature sensor, the temperaturesensor may be selectively removed from the eyewear and stored for futureuse.

At Step 310, the system receives data generated by the at least onesensor. In particular embodiments, the data generated by the at leastone sensor may include data for a heart rate, a heart rhythm orelectrical activity, a brain wave activity, a distance traveled, atemperature, an image, a sound, a speed traveled, a blood pressure, anoxygen saturation level, a near-field communication, a motion, anorientation, a geomagnetic field, a global position, an impact, amedicine regime, or any other suitable data.

In various embodiments, the system may receive the data substantiallyautomatically after the sensor generates the data. In some embodiments,the system may receive the data periodically (e.g., by the second, bythe minute, hourly, daily, etc.). For example, the system may receivethe data every thirty seconds throughout the day. In other embodiments,the system may receive the data after receiving an indication from theuser or a third party that the system should receive the data. Forinstance, the user may speak a voice command to the wearable devicerequesting that the device track the user's steps taken. In variousembodiments, the system may receive an indication from the user or athird party of when to have the system receive the data. For example,the system may receive an indication from the third party to have thesystem receive global positioning data at 8:00 a.m. and at 2:00 p.m.

In particular embodiments, the system may receive an indication from theuser or a third party to have particular data received from a particularsensor at the same time that the system receives second particular datafrom a second particular sensor. For example, when the system receivesdata that indicates that the user's speed has increased, the system mayat least partially in response to receiving the increased speed data,also obtain global position data of the user. In particular embodiments,the system may receive behavior data during a predefined time period.For instance, the system may receive behavior data for the user during apredefined time period when the user should not be moving (e.g., 11:00p.m. through 7:00 a.m. because the user should be sleeping). In variousembodiments, the system may receive the data when a sensor detectsmovement of the user. For example, the system may receive data from theglobal positioning system sensor when the accelerometer or the gyroscopedetects movement of the user.

In other embodiments, the data generated by the at least one sensor maybe whether the user experiences one of sudden acceleration and suddenimpact. In still other embodiments, the data generated by the at leastone sensor may be a heartbeat and whether the user is breathing. In yetother embodiments, the data generated by the at least one sensor may bea medicine regime associated with the user. For instance, the user orthe user's physician may manually input a medicine regime into thesystem by stating the name of the medicine while the user or the user'sphysician requests that the front-facing camera capture an image of themedicine and the medicine bottle. In some embodiments, the received datagenerated by the at least one sensor may be one or more images capturedby the forward facing camera. In other embodiments, the received datagenerated by the at least one sensor may be the level of one or moremedicines in the user's bloodstream.

In various embodiments, the system may receive data from a singlesensor. In other embodiments, the system may receive data from all ofthe sensors. In yet other embodiments, the system may receive multipledata from each of the sensors. In various embodiments, the system may beconfigured to receive first data from a first sensor at the same timethat it receives second data from a second sensor. For example, thesystem may be configured to receive a global position from the globalpositioning system sensor at the same time that it receives impact datafrom the impact sensor.

In particular embodiments, the system may store the received data. Invarious embodiments, the system may store the received datasubstantially automatically after receiving the data. In otherembodiments, the system may store the received data after receivingmanual input from the user or a third party requesting that the systemstore the data. In various embodiments, the system may store thereceived data for a specified period of time. For instance, the systemmay store the received data for a day, a month, a year, etc., in theBehavior Information Database 140. In some embodiments, the system maystore the received data on any suitable server, database, or device. Inother embodiments, the system may store the received data on the PatternAnalysis Server 120. In still other embodiments, the system may storethe received data in an account associated with the user. In variousembodiments, the system may store the received data with a timestamp ofwhen the data was received.

At Step 315, the system analyzes the data generated by the at least onesensor. In various embodiments, the system analyzes the data generatedby the at least one sensor substantially automatically after receivingthe generated data. In various embodiments, the system may analyze thedata periodically (e.g., by the second, by the minute, hourly, daily,etc.). For example, the system may analyze the data every thirty secondsthroughout the day. In other embodiments, the system may analyze thedata after receiving an indication from the user or a third party thatthe system should analyze data. For instance, the user may speak a voicecommand to the wearable device requesting that the device analyze theuser's steps taken. In various embodiments, the system may receive anindication from the user or a third party of when to have the systemanalyze the data. For example, the system may receive an indication fromthe third party to have the system analyze global positioning data at8:00 a.m. and at 2:00 p.m.

In other embodiments, the system may analyze the data to determine oneor more of (1) the type of medicine taken by the user; (2) the time themedicine is taken by the user; and (3) the dose of medicine taken by theuser. In still other embodiments, the step of analyzing the receiveddata further comprises detecting one or more pills in the one or moreimages, comparing the one or more detected pills found in the one ormore images to known images of pills stored in a database, identifyingthe one or more pills by matching the one or more pills from the one ormore images to the known images of pills stored in the database, anddetecting the time that the image was taken. In various embodiments, thesystem analyzes the level of the one or more medicines in the user'sbloodstream.

Then, at Step 320, the system determines the user's current movementsusing the received data in order to generate one or more movementpatterns for the user. In various embodiments, the system determines theuser's current movements substantially automatically after receiving thedata. In various embodiments, the system may determine the user'scurrent movements periodically (e.g., by the second, by the minute,hourly, daily, etc.). For example, the system may determine the user'scurrent movements every thirty seconds throughout the day. In otherembodiments, the system may determine the user's current movements afterreceiving an indication from the user or a third party that the systemshould analyze data. For instance, the user may speak a voice command tothe wearable device requesting that the device analyze the user's stepstaken. In various embodiments, the system may receive an indication fromthe user or a third party of when to have the system analyze the data.For example, the system may receive an indication from the third partyto have the system analyze global positioning data at 8:00 a.m. and at2:00 p.m.

In various embodiments, the system determines the user's currentmovements by calculating the number of steps taken by the user in aparticular day. In some embodiments, the system determines the user'scurrent movements by tracking the distance traveled by the user for aparticular day. In other embodiments, the system determines the user'scurrent movements by capturing a series of images from the front-facingcamera throughout the day. In still other embodiments, the systemdetermines the user's current movements by tracking the orientation ofthe user using the gyroscope. In particular embodiments, the currentmovements of the user may include actions such as lying down, falling,wandering, sitting, standing, walking, running, convulsing, shaking,balancing, etc.

In various embodiments, the user's current movements may include theuser's current location. For example, the user's current location may bean address, a geographic area, an intersection, a bus stop, a building,or any other suitable definable location. In other embodiments, theuser's current movements may help to indicate the user's current status(e.g., asleep, awake, conscious, unconscious, alive, deceased, stable,good, fair, serious, critical, injured, distressed, etc.). In someembodiments, the user's current behaviors may include compliance withprescribed treatment regimes. For instance, the user's current behaviorsmay include that the user has not been complying with prescribedtreatment regimes as captured through the front-facing camera of theuser not taking prescribed medicine.

In various embodiments, the system tracks current movements, currentlocation, current status, and current compliance to generate one or moremovement patterns, location patterns, status patterns, and compliancepatterns. In some embodiments, the system generates the one or morepatterns substantially automatically after the system determines theuser's current movements, location, status, and compliance. In someembodiments, the system may generate the patterns periodically (e.g., bythe second, by the minute, hourly, daily, weekly, monthly, etc.). Forexample, the system may generate a movement pattern for each month. Inother embodiments, the system may generate the pattern after receivingan indication from the user or a third party that the system shouldgenerate the pattern. For instance, the user may speak a voice commandto the wearable device requesting that the device generate a pattern forthe number of steps taken by the user for a typical day. In variousembodiments, the system may receive an indication from the user or athird party of when to have the system generate the patterns. Forexample, the system may receive an indication from the third party tohave the system generate a location pattern for the location of the userat 8:00 a.m. and at 2:00 p.m. for the previous month.

In various embodiments, the movement patterns may include one or moretypical movements made by the user. For example, the movement patternmay include that the user gets out of bed every morning. In particularembodiments, the location patterns may include one or more typicallocations of the user. For instance, the location pattern may includethat the user is at a first particular address in the morning, at asecond particular address during the day, and at the first particularaddress at night. In some embodiments, the status patterns may includeone or more typical statuses of the user. In example, the status patternmay include that the user is awake from 7:00 a.m. until 11:00 p.m. andasleep from 11:00 p.m. until 7:00 a.m. In other embodiments, thecompliance patterns may include one or more typical compliance schedulesof the user. For example, the compliance pattern may include that theuser is prescribed a medicine that the user takes every day in themorning with food. In yet other embodiments, the medicine regimepatterns may include one or more typical medicine regimes for the user.For instance, the medicine regime pattern may include that the usertakes a particular yellow pill, a particular white pill, and aparticular pink pill in the evening with food. In various embodiments,the system may include one or more typical levels of one or moremedicines in the user's bloodstream. For example, the typical level of aparticular medicine in the user's bloodstream may be a certain volume ata particular period of time.

In particular embodiments, the system may store the generated patternsin an account associated with the user. In some embodiments, thegenerated patterns may be accessible by the user or a third party. Forinstance, the generated patterns may be diagramed in a chart that isaccessible from the wearable device or from a computing device by theuser's physician. In various embodiments, the system may store thegenerated patterns in the Behavior Information Database 140. Inparticular embodiments, the system may store information in the BehaviorInformation Database 140 regarding past movement patterns associatedwith the user (e.g., when the user goes into different rooms in theuser's house, when the user eats, when the user takes a walk, thedestinations along the walk, etc.). In some embodiments, the system maystore information in the Behavior Information Database 140 regarding theuser's sleep patterns. In other embodiments, the system may storeinformation in the Behavior Information Database 140 regardinggeo-fences associated with the user. In still other embodiments, thesystem may store information in the Behavior Information Database 140regarding deviations to the user's typical behavior (e.g., movement)patterns.

At Step 325, the system compares the user's behaviors (e.g., movements)to the previously established one or more patterns for the user. In someembodiments, the system compares the user's movement to the previouslyestablished one or more movement patterns for the user substantiallyautomatically after the system receives the user's current movements. Insome embodiments, the system may compare the user's movement to thepreviously established one or more movement patterns periodically (e.g.,by the second, by the minute, hourly, daily, weekly, monthly, etc.). Forexample, the system may compare the user's current movement to thepreviously established one or more movement patterns every thirtyminutes throughout the day. In other embodiments, the system may comparethe user's movement to the previously established one or more movementpatterns after receiving an indication from the user or a third partythat the system should compare the user's movement to the previouslyestablished movement pattern. For instance, the user may speak a voicecommand to the wearable device requesting that the device compare theuser's movements for the current day to a movement pattern establishedthe previous month. In various embodiments, the system may receive anindication from the user or a third party of when to have the systemcompare the user's movements to the one or more patterns. For example,the system may receive an indication from the third party to have thesystem compare the user's current location to a location pattern for thelocation of the user at 8:00 a.m. and at 2:00 p.m. on a typical day.

In some embodiments, the system may compare the user's movements to apreviously established movement pattern by calculating the number ofsteps taken by the user in the particular day to a predetermined averagenumber of steps taken by the user in a day. In various embodiments, thesystem may compare the user's location to a previously establishedlocation pattern by determining the average location of the user at aparticular time of day. In other embodiments, the system may compare theuser's status to a previously established status pattern by determiningthe user's average status at particular times of day.

In still other embodiments, the system may compare the user's compliancewith a prescribed treatment regime by determining the user's averagecompliance with the prescribed treatment regime for a particular day. Inyet other embodiments, the system may compare the one or more of thetype of medicine taken; the time the medicine is taken; and the dose ofthe medicine taken to the stored medicine regime for the user. Invarious embodiments, the system may compare the level of one or moremedicines in the user's bloodstream by determining the average level ofthe one or more medicines in the user's bloodstream at particular timesof day.

In particular embodiments, the system may store the comparisons in anaccount associated with the user. In some embodiments, the comparisonsmay be accessible by the user or a third party. For instance, thecomparisons may be diagramed in a chart that is accessible from thewearable device or from a computing device by the user's physician.

Continuing to Step 330, the system detects one or more inconsistenciesbetween the user's current movements as compared to the previouslyestablished one or more patterns. In other embodiments, the system doesnot detect one or more inconsistencies between the user's currentmovements as compared to the previously established one or morepatterns. In various embodiments, the system may detect the one or moreinconsistencies by determining that the user's current movements areinconsistent with the previously established patterns. In particularembodiments, the user's current movements may be inconsistent withpreviously established patterns based on the current movements beingdifferent from the established patterns by a particular percentage. Forinstance, where the user's movement patterns establish that the userwalks a total of one mile a day, the system may determine that theuser's current movement of walking ½ mile for the day is inconsistentwith the user's previously established pattern of walking one mile a daybecause there is a difference of 50%.

In some embodiments, the user's current movements may be inconsistentwith the previously established movement patterns based on the user'scurrent movements not matching the previously established movementpatterns. For instance, for the movement pattern that includes that theuser gets out of bed every morning, where the system detects that theuser does not get out of bed on a particular morning, the system maydetermine that the user's current movements are inconsistent with thepreviously established pattern.

In other embodiments, the user's current movements may be inconsistentwith the previously established patterns based on the user's currentlocation not matching the previously established location patterns. Forexample, for the location pattern that includes the user at a firstparticular address in the morning, at a second particular address duringthe day, and at the first particular address at night, where the systemdetects that the user was not at the second particular address duringthe day, the system may determine that the user's current movements areinconsistent with the previously established pattern.

In still other embodiments, the user's current movements may beinconsistent with the previously established patterns based on theuser's current status not matching the previously established statuspatterns. For instance, for the status pattern that includes that theuser is awake from 7:00 a.m. until 11:00 p.m. and asleep from 11:00 p.m.until 7:00 a.m., where the system detects that the user is asleep from7:00 a.m. until 2:00 p.m., the system may determine that the user'scurrent movements are inconsistent with the previously establishedpattern.

In yet other embodiments, the system may detect one or moreinconsistencies between the medicine regime associated with the user andthe determined one or more of the type of medicine taken by the user,the time the medicine is taken by the user, and the dose of medicinetaken by the user. For instance, for a medicine regime that includesthat the user takes a particular pill having a particular color (e.g.,yellow), shape (e.g., triangular, square), and marking (e.g., the number17) in the evening with food, where the system detects that the user didnot take the particular yellow pill on a particular evening with food,the system may determine that the user's current movements areinconsistent with the previously established pattern.

In some embodiments, the system may detect one or more inconsistenciesbetween the level of the one or more medicines in the user's bloodstreamand the determined typical level of the one or more medicines in theuser's bloodstream. For example, for a typical level of a particularmedicine in the user's bloodstream that includes that the level is acertain volume at a particular period of time, where the system detectsthat the level of the medicine in the user's bloodstream is less thanthe typical level, the system may determine that the user's currentmovements are inconsistent with the previously established patterns.

At Step 335, the system notifies the user and/or a third party of thedetected one or more inconsistencies. In particular embodiments, inaddition to notifying at least one recipient selected from a groupconsisting of: the user and the third party, the system updates theuser's account to note that a notification was sent. In variousembodiments, the system notifies the user of the detected one or moreinconsistencies. In some embodiments, the system notifies the thirdparty of the detected one or more inconsistencies. In particularembodiments, the system may notify the user of the detected one or moreinconsistencies by displaying an image on the lens of the eyewear, or onanother display associated with the eyewear. In other embodiments, thesystem notifies the user of the one or more inconsistencies bycommunicating through a speaker to the user.

In various embodiments, the third party may be a relative of the user.In other embodiments, the third party may be a police department. Inparticular embodiments, the third party may be an ambulance service. Insome embodiments, the third party may be a physician. In still otherembodiments, the third party may be an independent living provider. Inyet other embodiments, the third party may be a particular caregiver ofthe user.

In some embodiments, the system notifies the user and/or the third partyof the one or more inconsistencies by sending a notification to theuser's and/or the third party's mobile devices. In particularembodiments, the system notifies the user and/or the third party of theone or more inconsistencies by email or text message. In otherembodiments, the system may notify the user and/or the third party of asingle inconsistency substantially immediately after the system detectsthe inconsistency between the user's current movements as compared tothe previously established one or more movement patterns. In yet otherembodiments, the system may notify the user and/or the third party ofall inconsistencies detected on a particular day at the end of that day.

In various embodiments, the system may notify the user and/or the thirdparty of the one or more inconsistencies after a particular event. Forexample, the system may notify the user if the system determines thatthe calculated number of steps of the user for a particular day is lessthan a predetermined percentage of the predetermined average number ofsteps taken by the user in a day. In some embodiments, the system maynotify the user and/or the third party of the one or moreinconsistencies after a particular period of time. For instance, thesystem may notify the third party of an association one hour after thesystem detects one or more inconsistencies between the user's currentmovements as compared to the previously established one or more movementpatterns. In still other embodiments, the system may notify the user ofthe one or more inconsistencies at a particular time of day. As anexample, the system may notify the user of one or more inconsistenciesbetween the user's current movements as compared to the previouslyestablished one or more movement patterns at the end of the day.

In various embodiments, at least partially in response to detectingwhether the user moves during the predefined time period, the system maynotify the user and/or third party if the user does not move during thepredefined time period. In other embodiments, at least partially inresponse to detecting one of sudden acceleration and sudden impact(e.g., such as that associated with a fall), the system may notify userand/or the third party that the user experienced the one of suddenacceleration and sudden impact. In some embodiments, at least partiallyin response to not detecting one of a heartbeat or breathing associatedwith the user, the system may notify the user and/or the third partythat the heartbeat and/or breathing of the user cannot be detected. Thismay indicate, for example, a medical emergency associated with the useror a malfunction of one or more system components.

In particular embodiments, the system may notify the user and/or thethird party of detected inconsistencies between the user's currentmovements and the previously established movement patterns. In someembodiments, the system may notify the user and/or the third party ofdetected inconsistencies between the user's current location and thepreviously established location patterns. In other embodiments, thesystem may notify the user and/or the third party of detectedinconsistencies between the user's current status and the previouslyestablished status patterns. In still other embodiments, the system maynotify the user and/or the third party of detected inconsistenciesbetween the user's current compliance and the previously establishedcompliance patterns. In yet other embodiments, the system may notify theuser and/or the third party of detected inconsistencies between theuser's current medicine regime and the previously established medicineregime patterns. In various embodiments, the system may notify at leastone recipient selected from a group consisting of: the user and thethird party of detected inconsistencies between the user's current levelof one or more medicines and the previously established typical one ormore levels of medicine.

In particular embodiments, the system may notify the user and/or thethird party of detected inconsistencies between the stored medicineregime and the one or more of the type of medicine taken, the time themedicine is taken, and the dose of medicine taken. In some embodiments,the system may notify at least one recipient selected from a groupconsisting of: the user and the third party if the user removes thewearable device for a predetermined period of time. In otherembodiments, the system may notify the user and/or the third party ifthe user does not consume food for a predetermined period of time. Inparticular embodiments, the system may notify the user and/or the thirdparty if the user does not consume liquids for a predetermined period oftime. In various embodiments, the system may notify the user and/or thethird party if the user's caloric intake is above or below apredetermined number of calories. In some embodiments, the system maynotify the user and/or the third party if the user's oxygen levels fallbelow a predetermined threshold. In other embodiments, the system maynotify the user and/or the third party if the user's blood sugar dropsbelow a predetermined threshold.

In various embodiments, the system, when executing the Behavior PatternAnalysis Module 300, may omit particular steps, perform particular stepsin an order other than the order presented above, or perform additionalsteps not discussed directly above.

Structure of the Eyewear

As shown in FIG. 4, eyewear 400, according to various embodiments,includes: (1) an eyewear frame 410; (2) a first temple 412; and (3) asecond temple 414. These various components are discussed in more detailbelow. In various embodiments, the eyewear 400 may be used as the one ormore health monitoring devices 156 shown in FIG. 1.

Eyewear Frame

Referring still to FIG. 4, eyewear 400, in various embodiments, includesany suitable eyewear frame 410 configured to support one or more lenses418, 420. In the embodiment shown in this figure, the eyewear frame 410has a first end 402 and a second end 404. The eyewear frame 410 may bemade of any suitable material such as metal, ceramic, polymers or anycombination thereof. In particular embodiments, the eyewear frame 410 isconfigured to support the first and second lenses 418, 420 about thefull perimeter of the first and second lenses 418, 420. In otherembodiments, the eyewear frame 410 may be configured to support thefirst and second lenses 418, 420 about only a portion of each respectivelens. In various embodiments, the eyewear frame 410 is configured tosupport a number of lenses other than two lenses (e.g., a single lens, aplurality of lenses, etc.). In particular embodiments, the lenses 418,420 may include prescription lenses, sunglass lenses, or any othersuitable type of lens (e.g., reading lenses, non-prescription lenses),which may be formed from glass or polymers.

The eyewear frame 410 includes a first and second nose pad 422 (notshown in figures), 424, which may be configured to maintain the eyewear400 adjacent the front of a wearer's face such that the lenses 418, 420are positioned substantially in front of the wearer's eyes while thewearer is wearing the eyewear 400. In particular embodiments, the nosepads 422, 424 may comprise a material that is configured to becomfortable when worn by the wearer (e.g., rubber, etc.). In otherembodiments, the nose pads may include any other suitable material(e.g., plastic, metal, etc.). In still other embodiments, the nose padsmay be integrally formed with the frame 410.

The eyewear frame 410 includes a first and second hinge 426, 428 thatattach the first and second temples 412, 414 to the frame first andsecond ends 402, 404, respectively. In various embodiments, the hingesmay be formed by any suitable connection (e.g., tongue and groove, balland socket, spring hinge, etc.). In particular embodiments, the firsthinge 426 may be welded to, or integrally formed with, the frame 410 andthe first temple 412 and the second hinge 428 may be welded to, orintegrally formed with, the frame 410 and the second temple 414.

First and Second Temples

As shown in FIG. 4, the first temple 412, according to variousembodiments, is rotatably connected to the frame 410 at a right angle toextend the first temple 412 substantially perpendicular, substantiallyparallel, or anywhere in between the right angle to the frame 410. Thefirst temple 412 has a first and second end 412 a, 412 b. Proximate thefirst temple second end 412 b, the first temple 412 includes an earpiece413 configured to be supported by a wearer's ear. Similarly, the secondtemple 414, according to various embodiments, is rotatably connected tothe frame 410 at a right angle to extend the second temple 414substantially perpendicular, substantially parallel, or anywhere inbetween the right angle to the frame 410. The second temple 414 has afirst and second end 414 a, 414 b. Proximate the second temple secondend 414 b, the second temple 414 includes an earpiece 415 configured tobe supported by a wearer's ear.

Sensors

In various embodiments, the second temple 414 has one or more sensors430 connected to the second temple 414. In various embodiments, the oneor more sensors 430 may be coupled to the frame 410, the first andsecond temples 412, 414, the first and second lenses 418, 410, or anyother portion of the eyewear 400 in any suitable way. For instance, theone or more sensors 430 may be embedded into the eyewear 400, coupled tothe eyewear 400, and/or operatively coupled to the eyewear 400. Invarious embodiments, the one or more sensors may be formed at any pointalong the eyewear 400. For instance, a fingerprint reader may bedisposed adjacent the first temple of the eyewear 400. In variousembodiments, the one or more sensors may be formed in any shape. Inaddition, the one or more sensors may be formed on the inner (back)surface of the frame 410, the first and second temples 412, 414, thefirst and second lenses 418, 410, or any other portion of the eyewear400. In other embodiments, the one or more sensors may be formed on theouter (front) surface of the frame 410, the first and second temples412, 414, the first and second lenses 418, 410, or any other portion ofthe eyewear 400.

In various embodiments, the one or more sensors 430 that are coupled tothe eyewear (or other wearable device) are adapted to detect one or morecharacteristics of the eyewear or a wearer of the eyewear, wherein theone or more characteristics of the wearer are associated with thewearer's identity. In various embodiments, the one or more sensorscoupled to the eyewear or other health monitoring device may include,for example, one or more of the following: a near-field communicationsensor, a gyroscope, a Bluetooth chip, a GPS unit, an RFID tag (passiveor active), a fingerprint reader, an iris reader, a retinal scanner, avoice recognition sensor, a heart rate monitor, an electrocardiogram(EKG), an electroencephalogram (EEG), a pedometer, a thermometer, afront-facing camera, an eye-facing camera, a microphone, anaccelerometer, a magnetometer, a blood pressure sensor, a pulseoximeter, a skin conductance response sensor, any suitable biometricreader, or any other suitable sensor. In some embodiments, the one ormore sensors may include a unique shape, a unique code, or a uniquedesign physically inscribed into the eyewear that may be readable by anindividual or a remote computing device. In particular embodiments, thesensors coupled to the eyewear may include one or more electroniccommunications devices such as a near field communication sensor, aBluetooth chip, an active RFID, and a GPS unit.

In various embodiments, the one or more sensors are coupled to acomputing device that is associated with (e.g., embedded within,attached to) the eyewear or other wearable device. In particularembodiments, the eyewear or other wearable device comprises at least oneprocessor, computer memory, suitable wireless communications components(e.g., a Bluetooth chip) and a power supply for powering the wearabledevice and/or the various sensors.

As noted above, the one or more sensors may be coupled to a Bluetoothdevice that is configured to transmit the one or more signals to ahandheld wireless device, and the step of using the eyewear to confirmthe identity of the wearer of the eyewear (discussed above in referenceto Step 310) further comprises receiving the one or more signals fromthe wireless handheld device (e.g., via the Internet). In particularembodiments, one or more of the sensors may be detachable from theeyewear. For instance, if a wearer does not need a temperature sensor orother particular sensor, the sensor may be removed from the eyewear.

Exemplary User Experience

Independent Living of Elderly

In a particular example of a user using the Behavior Pattern AnalysisModule 300, the user may put on the wearable device in the morning andcontinue to wear the device throughout the day. In various embodiments,the wearable device may be operatively coupled (e.g., via a suitablewireless or wired connection) to a smart phone, a laptop, a desktopcomputer, an automated dialing apparatus, or any other computing devicethat can receive signals from the wearable device and either transmitthe signals to a central system (e.g., via a wireless or wired telephonenetwork) or analyze the signals and make decisions based on the receivedsignals (e.g., call for help, notify a loved one, etc.). During thistime, the system will track the movements of the user using the motionsensor, the accelerometer, the global positioning sensor, the gyroscope,and the front-facing camera. In this example, the user may be an elderlyor infirm person that desires to live independently, but the personrequires monitoring for events that deviate from the person's normalroutine. Thus, by wearing the wearable device throughout the day, thedevice is able to track the user's movements and create certain patternsbased on these movements for the user. The system may then store thesepatterns in a database while continuing to track the user's movements.Where the system detects that the user has deviated from the previouslyestablished pattern, the system may notify the user's physician, forexample directly from the wearable device, or via the connectedsmartphone, computer or the automated dialing apparatus. Such deviationsfrom the previously established pattern may include that the user falls,that the user wanders beyond preset boundaries (e.g., defined by one ormore geofences), that the user begins sleeping longer than usual, thatthe user stops moving, or any other deviation from a previouslyestablished pattern of the user's normal routine.

For example, the user may be an Alzheimer's patient that has lucidmoments and moments of memory loss. As has been established as amovement pattern by the wearable device, the patient takes a walk aroundthe block every morning. However, if the patient wanders two blocks overand outside of the user's predetermined geo-fenced area, which is adeviation from the patient's normal pattern of movements, the system mayalert the patient's caregiver of the inconsistency between the patient'scurrent actions and the previously established patterns.

Monitor Compliance with Medicine Regime

The system, in a particular example, will also monitor the user'scompliance with a medicine regime. In order to establish the user'smedicine regime pattern, the user may wear the wearable device to detectwhen the user takes medicine and what medicine is taken using thefront-facing camera. The user may also speak the name of the medicine asthe wearable device captures an image of the medicine the user istaking. The system is then able to establish a pattern of the usertaking blood pressure medicine every morning after monitoring the userfor a week. The system may then monitor the user's current medicineintake to compare the medicines the user takes and the time that theuser takes the medicine to the previously established medicine regimepattern. If the user fails to take the blood pressure medicine on aparticular morning, the system may notify the user, the user'scaregiver, a health care provider, or a third party that the user hasdeviated from the previously established medicine regime.

CONCLUSION

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains, havingthe benefit of the teaching presented in the foregoing descriptions andthe associated drawings. Therefore, it is to be understood that theinvention is not to be limited to the specific embodiments disclosed andthat modifications and other embodiments are intended to be includedwithin the scope of the appended claims. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for the purposes of limitation.

What is claimed is:
 1. A computer-implemented method of monitoring thewellbeing of an individual, the method comprising the steps of: a.providing a user with computerized eyewear comprising at least onesensor for monitoring the motion of the user; b. receiving, by one ormore processors, an indication from the user, for the at least onesensor to generate a first set of data identifying one or more movementpatterns for the user; c. in response to receiving the indication,collecting, by one or more processors, via the at least one sensor, thefirst set of data; d. generating, by one or more processors, anestablished one or more movement patterns for the user based on thefirst set of data generated by the at least one sensor; e. receiving, byone or more processors, a second set of data generated by the at leastone sensor after the established one or more movement patterns have beengenerated; f. at least partially in response to receiving the second setof data generated by the at least one sensor, determining, by one ormore processors, the user's movements using the received second set ofdata; g. detecting, by one or more processors, one or moreinconsistencies between the user's movements based on the second set ofdata as compared to the previously established one or more movementpatterns for the user based on the first set of data; h. at leastpartially in response to detecting the one more inconsistencies,notifying, by one or more processors, at least one recipient of the oneor more inconsistencies, where the at least one recipient is a recipientselected from a group consisting of: the user and a third party.
 2. Thecomputer-implemented method of claim 1, wherein the at least one sensorcomprises at least one sensor selected from a group consisting of: a. amotion sensor; b. an accelerometer; c. a gyroscope; d. a geomagneticsensor; e. a global positioning system sensor; f. an impact sensor; g. amicrophone; h. a forward facing camera; i. a heart rate monitor; j. apulse oximeter; k. a blood alcohol monitor; l. a respiratory ratesensor; and m. a transdermal sensor.
 3. The computer-implemented methodof claim 2, wherein the at least one sensor comprises at least onesensor selected from a group consisting of: a motion sensor, anaccelerometer, a global positioning sensor, a gyroscope, and a forwardfacing camera.
 4. The computer-implemented method of claim 2, whereinthe method further comprises the step of: a. calculating, by aprocessor, a number of steps taken by the user in a particular day; b.at least partially in response to calculating the number of steps,comparing, by a processor, the calculated number of steps taken by theuser in the particular day to a predetermined average number of stepstaken by the user in a day; and c. at least partially in response tocomparing the calculated number of steps to the predetermined averagenumber of steps, notifying the user or a third party if the calculatednumber of steps in the particular day is less than a predeterminedpercentage of the predetermined average number of steps taken by theuser in a day.
 5. The computer-implemented method of claim 2, furthercomprising the steps of: a. detecting, by a processor, whether the usermoves during a predefined time period; and b. at least partially inresponse to detecting whether the user moves during the predefined timeperiod, notifying, by a processor, the at least one recipient selectedfrom a group consisting of: the user or a third party if the user doesnot move during the predefined time period.
 6. The computer-implementedmethod of claim 2, further comprising the steps of: a. detecting, by aprocessor, from the received data generated by the at least one sensorif the user experiences a sudden acceleration or sudden impact; and b.at least partially in response to detecting that the user hasexperienced a sudden acceleration or sudden impact, notifying, by aprocessor, the user or a third party that the user experienced thesudden acceleration or sudden impact.
 7. The computer-implemented methodof claim 2, further comprising the steps of: a. detecting, by aprocessor, from the received data generated by the at least one sensor:(1) whether the user is breathing; and (2) whether the user's heart isbeating; and b. at least partially in response to determining that theuser is not breathing or that the user's heart is not beating, sending anotification to a third party.
 8. The computer-implemented method ofclaim 2, further comprising the steps of: a. receiving, by a processor,from the user or third party, a medicine regime associated with theuser; b. storing, by a processor, the medicine regime in memory; c.receiving, by a processor, data generated by a forward facing cameraassociated with the computerized eyewear; d. analyzing, by a processor,the received data to determine data selected from a group consisting ofone or more: i. types of medicine taken by the user; ii. times themedicine is taken by the user; and iii. doses of the medicine taken bythe user; e. at least partially in response to analyzing the receiveddata, comparing, by a processor, the one or more of the types ofmedicine taken, the one or more times the medicine is taken, or the oneor more doses of medicine taken to the stored medicine regime for theuser; f. at least partially in response to comparing the one or more ofthe type of medicine taken, the time the medicine is taken and the doseof medicine taken, identifying, by a processor, one or moreinconsistencies between the stored medicine regime, and the one or moretypes of medicine taken, the one or more times the medicine is taken, orthe one or more doses of medicine taken; g. at least partially inresponse to identifying the one or more inconsistencies between themedicine regime and the one or more of the types of medicine taken, theone or more times the medicine is taken, or the one or more doses ofmedicine taken, sending an alert to the user or a third party of the oneor more inconsistencies.
 9. The computer-implemented method of claim 8,wherein: a. the data generated comprises one or more images captured bythe forward facing camera; b. the step of analyzing the received datafurther comprises: i. detecting, by a processor, one or more pills inthe one or more images; ii. comparing, by a processor, the one or moredetected pills found in the one or more images to one or more knownimages of pills stored in a database; iii. identifying, by a processor,the one or more pills by matching the one or more pills from the one ormore images to the one or more known images of pills stored in thedatabase; and iv. detecting, by a processor, a time that the one or moreimages were taken.
 10. The computer-implemented method of claim 1,wherein the indicator is defined by the user. 11-19. (canceled)
 20. Acomputer-implemented method of monitoring the wellbeing of anindividual, the method comprising the steps of: a. providing a user withcomputerized eyewear comprising at least one sensor for monitoring themotion of the user; b. receiving a command for the at least one sensorto generate a first set of data identifying one or more user-definedmovement patterns for the user; c. in response to receiving the command,collecting, by one or more processors, via the at least one sensor, thefirst set of data; d. generating, by one or more processors, anestablished one or more movement patterns for the user based on thefirst set of data generated by the at least one sensor; e. receiving, byone or more processors, a second set of data generated by the at leastone sensor after the established one or more movement patterns have beengenerated; f. at least partially in response to receiving the second setof data generated by the at least one sensor, determining, by one ormore processors, the user's movements using the received second set ofdata; g. detecting, by one or more processors, one or moreinconsistencies between the user's movements based on the second set ofdata as compared to the previously established one or more movementpatterns for the user based on the first set of data; h. at leastpartially in response to detecting the one more inconsistencies,notifying, by one or more processors, at least one recipient of the oneor more inconsistencies, where the at least one recipient is a recipientselected from a group consisting of: the user and a third party.
 21. Thecomputer-implemented method of claim 20, wherein the at least one sensorcomprises at least one sensor selected from a group consisting of: a. amotion sensor; b. an accelerometer; c. a gyroscope; d. a geomagneticsensor; e. a global positioning system sensor; f. an impact sensor; g. amicrophone; h. a forward facing camera; i. a heart rate monitor; j. apulse oximeter; k. a blood alcohol monitor; l. a respiratory ratesensor; and m. a transdermal sensor.
 22. The computer-implemented methodof claim 21, wherein the at least one sensor comprises at least onesensor selected from a group consisting of: a motion sensor, anaccelerometer, a global positioning sensor, a gyroscope, and a forwardfacing camera.
 23. The computer-implemented method of claim 21, whereinthe method further comprises the step of: a. calculating, by aprocessor, a number of steps taken by the user in a particular day; b.at least partially in response to calculating the number of steps,comparing, by a processor, the calculated number of steps taken by theuser in the particular day to a predetermined average number of stepstaken by the user in a day; and c. at least partially in response tocomparing the calculated number of steps to the predetermined averagenumber of steps, notifying the user or a third party if the calculatednumber of steps in the particular day is less than a predeterminedpercentage of the predetermined average number of steps taken by theuser in a day.
 24. The computer-implemented method of claim 21, furthercomprising the steps of: a. detecting, by a processor, whether the usermoves during a predefined time period; and b. at least partially inresponse to detecting whether the user moves during the predefined timeperiod, notifying, by a processor, the at least one recipient selectedfrom a group consisting of: the user or a third party if the user doesnot move during the predefined time period.
 25. The computer-implementedmethod of claim 21, further comprising the steps of: a. detecting, by aprocessor, from the received data generated by the at least one sensorif the user experiences a sudden acceleration or sudden impact; and b.at least partially in response to detecting that the user hasexperienced a sudden acceleration or sudden impact, notifying, by aprocessor, the user or a third party that the user experienced thesudden acceleration or sudden impact.
 26. The computer-implementedmethod of claim 21, further comprising the steps of: a. detecting, by aprocessor, from the received data generated by the at least one sensor:(1) whether the user is breathing; and (2) whether the user's heart isbeating; and b. at least partially in response to determining that theuser is not breathing or that the user's heart is not beating, sending anotification to a third party.
 27. The computer-implemented method ofclaim 21, further comprising the steps of: a. receiving, by a processor,from the user or third party, a medicine regime associated with theuser; b. storing, by a processor, the medicine regime in memory; c.receiving, by a processor, data generated by a forward facing cameraassociated with the computerized eyewear; d. analyzing, by a processor,the received data to determine data selected from a group consisting ofone or more: i. types of medicine taken by the user; ii. times themedicine is taken by the user; and iii. doses of the medicine taken bythe user; e. at least partially in response to analyzing the receiveddata, comparing, by a processor, the one or more of the types ofmedicine taken, the one or more times the medicine is taken, or the oneor more doses of medicine taken to the stored medicine regime for theuser; f. at least partially in response to comparing the one or more ofthe type of medicine taken, the time the medicine is taken and the doseof medicine taken, identifying, by a processor, one or moreinconsistencies between the stored medicine regime, and the one or moretypes of medicine taken, the one or more times the medicine is taken, orthe one or more doses of medicine taken; g. at least partially inresponse to identifying the one or more inconsistencies between themedicine regime and the one or more of the types of medicine taken, theone or more times the medicine is taken, or the one or more doses ofmedicine taken, sending an alert to the user or a third party of the oneor more inconsistencies.
 28. The computer-implemented method of claim27, wherein: a. the data generated comprises one or more images capturedby the forward facing camera; b. the step of analyzing the received datafurther comprises: i. detecting, by a processor, one or more pills inthe one or more images; ii. comparing, by a processor, the one or moredetected pills found in the one or more images to one or more knownimages of pills stored in a database; iii. identifying, by a processor,the one or more pills by matching the one or more pills from the one ormore images to the one or more known images of pills stored in thedatabase; and iv. detecting, by a processor, a time that the one or moreimages were taken.
 29. The computer-implemented method of claim 20,wherein the command is defined by the user.